For more than 3 months, the team iterated on the designs of the data visualizations. It was a three-stage approach: data insight discovery, low-fidelity design, and high-fidelity design.
Data insight discovery involved looking at the raw Excel data and uncovering patterns. If a pattern was found with particular variables, a low-fidelity design was constructed. After receiving feedback from designers, a high-fidelity design with technical specifications was developed.
In the iteration process, we faced several design challenges:
1. How might we make visualizations understandable and insightful to both the general public and the challenge participants?
2. Can a single data variable be shown through two different visual channels (i.e., size, x/y positioning)?
3. How do we balance aesthetics and ease of comprehension? How explicit do our designs have to be to ensure comprehension?